Connected devices are now a new normal in our lives, which means smart virtual assistants, fridges, routers, clocks, or doorbells are mundane, everyday things. Internet of things (IoT) systems continue to grow in popularity due to the lockdown, Intetics blog says. During the pandemic, many industries have needed to transform their communications, delivery, maintenance, and other processes.
Gartner’s recent survey reports that despite the impact of COVID-19, 47% of businesses will enlarge their investments in IoT. So, why are IoT solutions indispensable for unleashing the full potential of your business?
Under the hood of IoT and ML
Let’s go over the working principles of IoT and machine learning (ML) systems, which consist of the following key components:
- Hardware parts
- Processors, sensors, storage places and software
- Data reception components and protocols
- Analytics to glean critical insights from data
Developing a custom IoT solution depends on a plethora of edge devices located in offices, homes, warehouses, aboard ships or anywhere else.
To address complicated requirements, vendors of network automation and orchestration tools create a wide range of IoT solutions and services that support different types of edge devices. Many solutions only work in constrained IoT environments, based on operator or device models, or limited third-party platforms. Some also focus on specific verticals, such as utility management, smart grids in retail, cargo tracking in logistics, or energy management.
You should be wondering where machine-learning algorithms fit into the picture. IoT systems collect data through its many sensors, but massive amounts of data would be useless without adequate processing methods. Raw data requires thorough analyses and appropriate organizing before you can make valuable use of it.
Machine-learning and deep-learning algorithms allow computers to mimic how the human brain works, processing big data and generating inference. But machines require algorithms to perform tasks, learn and self-regulate in some cases. By adopting more and more nuances of human-like behavior, they might become more sophisticated.
IoT and machine-learning applications work as machine-to-machine (M2M) communication systems. А computer network protocol connects one machine — be it a mobile phone, mobile gateway, or an electronic component — to other IoT devices, like sensors, without having a human user interface in sight.
The training process of machine learning models and IoT systems starts with raw data that a computer receives as input, then finds correlations and provides an output that makes sense. The more data it processes, the more complicated the tasks it can perform, generating valuable feedback. An analogy that could be made is that IoT and machine learning are connected similarly to our bodies and minds, which work together to collect and analyze data from our senses.
Benefits of IoT implementation for your business
An obvious application of machine learning for IoT is the automation of data processing. But it is not the only possible use case combining IoT and machine learning. Another widely used capability of machine learning or deep learning and IoT is the predictive potential they provide to make the most out of raw data. Let’s have a look at some notable use cases.
1. Manufacturing and supply-chain optimization
IoT platforms for global supply chains are characterized by a modular, decentralized, scalable and connected architecture. Using those advantages, companies may form a dynamic network and monitor their manufacturing and supply-chain operations across the globe.
Connected to machinery and augmented with ML models, smart solutions help analyze up and downtime. Also, IoT and machine learning systems assist with overseeing and documenting the quality and performance history of a product. Engineers have access to technical information at any time, and they can make quick adjustments to the product or add new functionality in no time.
2. Creating early warning and detection signals
Predictive maintenance is the field in which machine learning and IoT applications bring tangible benefits such as reduced maintenance costs and optimal usage of resources and equipment. For instance, a forklift driver at a warehouse may take sharp turns and risk damaging the goods on the shelves; too much material might be wasted on a production line, or an old piece of equipment may go out of order at the worst possible time.
In each of those cases, IoT using deep learning will help notice those flaws in advance and send timely warnings to the driver, give feedback about the optimal amount of material to be used for production, or detect possible equipment malfunctions before they occur.
3. Demand prediction
Another valuable case of employing ML is the optimization of parking space. A smart system can define when a parking lot is busy and help prevent the frustration of drivers by improving the parking experience. The algorithm requires data gathered from IoT sensors and cameras to create a model capable of predicting driver behavior or accurately forecasting when a particular parking spot will be free, or if it is likely to be busy all day.
Warehouses are a good example, too. An algorithm notifies the staff which shelves will soon need restocking, prompts them to reorganize and optimize inner space, and helps them navigate through storage facilities.
Existing traditional solutions cannot address the new reality of an autonomous world that requires smart innovations, which has resulted in the proliferation of IoT devices. Automation technologies and connected devices make the relationship between businesses and their customers more dynamic. New solutions are adept at handling large amounts of data instantly and do it using cloud platforms, without depending on a single data center.
Conclusion
Myriads of sensors worldwide gather enormous amounts of data, but most of the data saved on servers is useless and raw, occupying considerable storage space. Is that the case at your company? To harness the data, you need savvy experts and proven tools, such as ML, which can help you solve your data challenges.
It’s up to you to decide whether you need up-to-date technology to develop your business, but it seems there’s no reason to neglect the potential of IoT and machine learning, technologies that are progressing by the day.